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Become a Data Engineer in 2025: 3-Step ARC Learning Framework
Become a Data Engineer in 2025: 3-Step ARC Learning Framework
Hello friends, welcome to this video where I discuss the ARC Framework for learning new technologies and becoming a successful data engineer. In this video, I’ll guide you through the essential components of this framework, which will help you structure your learning and build a strong foundation in data engineering.
What is the ARC Framework?
The ARC Framework comprises three parts:
1. Acquire – Learn and master the tools and technologies needed for data engineering.
2. Refine – Improve your understanding and apply these tools in real-world scenarios.
3. Contribute – Share your knowledge, contribute to open-source projects, or mentor others to solidify your expertise.
Acquire: Mastering Key Tools
In the Acquire phase, you’ll focus on these tools:
Storage: Delta Lake for managing large-scale data with ACID transactions.
Data Orchestration: Tools like Apache Airflow and Prefect for automating workflows.
Data Preprocessing: Apache Spark for processing and transforming large datasets.
Streaming Analytics: Apache Kafka and Apache Druid for real-time analytics.
Reporting: Tools like Apache Superset, BIRT, and Power BI for creating dashboards and reports.
Machine Learning: Frameworks like TensorFlow or those that integrate with Spark for scalable ML solutions.
Timestamps:
00:00 - Introduction to the ARC Framework
00:35 - Overview of the Acquire Phase
01:22 - Tools for Storage (Delta Lake)
01:31 - Data Orchestration (Airflow/Prefect)
02:00 - Data Preprocessing with Apache Spark
02:15 - Streaming Analytics (Kafka, Druid)
02:58 - Reporting Tools (Superset, BIRT, Power BI)
4:40 - Machine Learning (TensorFlow and Spark ML)
5:10 - Basics (Python, Java, CSS, Javascript)
5:58 - Docker, Kubernetes and Git
7:10 - Refine: Learning and applying tools in Real-World Scenarios
10:22 - Contribute: Sharing Knowledge and Open Source
This video is perfect for beginners or anyone transitioning into data engineering. By following the ARC Framework, you’ll have a clear roadmap to mastering the key technologies in the field.
Don’t forget to like, subscribe, and hit the bell icon to stay updated with more tech-focused content!
Let’s start building your data engineering journey with the ARC Framework today!
Видео Become a Data Engineer in 2025: 3-Step ARC Learning Framework канала Shantanu Khond
Hello friends, welcome to this video where I discuss the ARC Framework for learning new technologies and becoming a successful data engineer. In this video, I’ll guide you through the essential components of this framework, which will help you structure your learning and build a strong foundation in data engineering.
What is the ARC Framework?
The ARC Framework comprises three parts:
1. Acquire – Learn and master the tools and technologies needed for data engineering.
2. Refine – Improve your understanding and apply these tools in real-world scenarios.
3. Contribute – Share your knowledge, contribute to open-source projects, or mentor others to solidify your expertise.
Acquire: Mastering Key Tools
In the Acquire phase, you’ll focus on these tools:
Storage: Delta Lake for managing large-scale data with ACID transactions.
Data Orchestration: Tools like Apache Airflow and Prefect for automating workflows.
Data Preprocessing: Apache Spark for processing and transforming large datasets.
Streaming Analytics: Apache Kafka and Apache Druid for real-time analytics.
Reporting: Tools like Apache Superset, BIRT, and Power BI for creating dashboards and reports.
Machine Learning: Frameworks like TensorFlow or those that integrate with Spark for scalable ML solutions.
Timestamps:
00:00 - Introduction to the ARC Framework
00:35 - Overview of the Acquire Phase
01:22 - Tools for Storage (Delta Lake)
01:31 - Data Orchestration (Airflow/Prefect)
02:00 - Data Preprocessing with Apache Spark
02:15 - Streaming Analytics (Kafka, Druid)
02:58 - Reporting Tools (Superset, BIRT, Power BI)
4:40 - Machine Learning (TensorFlow and Spark ML)
5:10 - Basics (Python, Java, CSS, Javascript)
5:58 - Docker, Kubernetes and Git
7:10 - Refine: Learning and applying tools in Real-World Scenarios
10:22 - Contribute: Sharing Knowledge and Open Source
This video is perfect for beginners or anyone transitioning into data engineering. By following the ARC Framework, you’ll have a clear roadmap to mastering the key technologies in the field.
Don’t forget to like, subscribe, and hit the bell icon to stay updated with more tech-focused content!
Let’s start building your data engineering journey with the ARC Framework today!
Видео Become a Data Engineer in 2025: 3-Step ARC Learning Framework канала Shantanu Khond
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31 декабря 2024 г. 23:31:15
00:19:04
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